Journal of Shandong University (Health Sciences) ›› 2025, Vol. 63 ›› Issue (8): 69-78.doi: 10.6040/j.issn.1671-7554.0.2025.0119

• Clinical Research • Previous Articles    

Application of metabolomic risk score in predicting cardiovascular outcomes in patients with type 2 diabetes mellitus

SHEN Lujia1,2, LU Tianwei3, GONG Weiming1,2, ZHAO Yansong1,2, WANG Shukang1,2, YUAN Zhongshang1,2   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. National Institute of Health and Medical Big Data, Jinan 250003, Shandong, China;
    3. Department of Maternal and Child Health, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China
  • Published:2025-08-25

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Abstract: Objective To identify metabolites associated with myocardial infarction, heart failure, and ischemic stroke respectively in a population with type 2 diabetes mellitus, thereby constructing a metabolomic risk score and evaluating whether incorporation of the metabolomic risk score into a traditional clinical model improves the models predictive accuracy. Methods Using data from the UK Biobank cohort, a multivariate Cox proportional risk regression model was applied to screen for metabolites associated with myocardial infarction, heart failure, and ischemic stroke, respectively, and a time-truncated sensitivity analysis was performed. Subsequently, metabolomic risk scores were constructed based on the LightGBM algorithm, and finally the scores were incorporated into traditional clinical models for model evaluation. Results Multivariate Cox proportional risk regression models and time-truncated sensitivity analyses identified a total of 119, 77, and 12 metabolites associated with myocardial infarction, heart failure, and ischemic stroke, respectively, and these metabolites were then used to construct metabolomic risk scores. Incorporation of the constructed metabolomic risk scores into traditional clinical models significantly improved model prediction performance, with model AUCs improving to 0.804, 0.900, and 0.844 for the three diseases respectively, and the improvements were 0.145, 0.198, and 0.188, compared to utilizing only traditional clinical models. In addition, the sensitivity and specificity results showed that the three models had high prediction accuracy(sensitivity: 0.706, 0.804, 0.801; specificity: 0.763, 0.861, 0.722), and the calibration curves and decision curves likewise showed that the models had good prediction performance. Conclusion Incorporation of metabolomic risk scores into traditional clinical models can significantly improve the predictive accuracy of cardiovascular disease in the type 2 diabetes mellitus population.

Key words: Type 2 diabetes mellitus, Myocardial infarction, Heart failure, Ischemic stroke, LightGBM, Metabolomic risk score, Predictive modeling

CLC Number: 

  • R743.3
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